Dataset and ANFIS model prediction of the performance of graphene nano-LPG in domestic refrigerator system

T. O. Babarinde; D. M. Madyira

doi:10.1016/j.dib.2022.108548

Dataset and ANFIS model prediction of the performance of graphene nano-LPG in domestic refrigerator system

T. O. Babarinde, D. M. Madyira

Mechanical Engineering Science

University of Johannesburg

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

LPG's steady-state performance in a base lubricant and a graphene nanolubricant was investigated in this study. Step-by-step processes and procedures for preparing graphene nanolubricant concentrations and replacing them for the base lubricant in a domestic refrigerator system were presented as the measuring devices necessary and their uncertainties. The experimental dataset and the training and testing datasets for Adaptive Neuro-fuzzy Inference System. (ANFIS) are available. The use of an ANFIS approach model to forecast graphene nanolubricant performance in a domestic refrigerator is described. The Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) are also available as statistical performance indicators for the ANFIS model prediction.

Original language	English
Article number	108548
Journal	Data in Brief
Volume	44
DOIs	https://doi.org/10.1016/j.dib.2022.108548
Publication status	Published - Oct 2022

Keywords

ANFIS testing data
ANFIS training data
Cooling capacity
COP
Experimental data
LPG
Nanolubricant
Power consumption

ASJC Scopus subject areas

Multidisciplinary

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10.1016/j.dib.2022.108548

Cite this

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title = "Dataset and ANFIS model prediction of the performance of graphene nano-LPG in domestic refrigerator system",

abstract = "LPG's steady-state performance in a base lubricant and a graphene nanolubricant was investigated in this study. Step-by-step processes and procedures for preparing graphene nanolubricant concentrations and replacing them for the base lubricant in a domestic refrigerator system were presented as the measuring devices necessary and their uncertainties. The experimental dataset and the training and testing datasets for Adaptive Neuro-fuzzy Inference System. (ANFIS) are available. The use of an ANFIS approach model to forecast graphene nanolubricant performance in a domestic refrigerator is described. The Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) are also available as statistical performance indicators for the ANFIS model prediction.",

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AU - Babarinde, T. O.

AU - Madyira, D. M.

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N2 - LPG's steady-state performance in a base lubricant and a graphene nanolubricant was investigated in this study. Step-by-step processes and procedures for preparing graphene nanolubricant concentrations and replacing them for the base lubricant in a domestic refrigerator system were presented as the measuring devices necessary and their uncertainties. The experimental dataset and the training and testing datasets for Adaptive Neuro-fuzzy Inference System. (ANFIS) are available. The use of an ANFIS approach model to forecast graphene nanolubricant performance in a domestic refrigerator is described. The Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) are also available as statistical performance indicators for the ANFIS model prediction.

AB - LPG's steady-state performance in a base lubricant and a graphene nanolubricant was investigated in this study. Step-by-step processes and procedures for preparing graphene nanolubricant concentrations and replacing them for the base lubricant in a domestic refrigerator system were presented as the measuring devices necessary and their uncertainties. The experimental dataset and the training and testing datasets for Adaptive Neuro-fuzzy Inference System. (ANFIS) are available. The use of an ANFIS approach model to forecast graphene nanolubricant performance in a domestic refrigerator is described. The Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) are also available as statistical performance indicators for the ANFIS model prediction.

KW - ANFIS testing data

KW - ANFIS training data

KW - Cooling capacity

KW - COP

KW - Experimental data

KW - LPG

KW - Nanolubricant

KW - Power consumption

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Dataset and ANFIS model prediction of the performance of graphene nano-LPG in domestic refrigerator system

Abstract

Keywords

ASJC Scopus subject areas

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